+#' computeGridLambda
+#'
#' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
+#'
#' @param phiInit value for phi
#' @param rhoInit value for rho
#' @param piInit value for pi
#' @param X matrix of covariates (of size n*p)
#' @param Y matrix of responses (of size n*m)
#' @param gamma power of weights in the penalty
-#' @param mini minimum number of iterations in EM algorithm
-#' @param maxi maximum number of iterations in EM algorithm
-#' @param tau threshold to stop EM algorithm
+#' @param mini minimum number of iterations in EM algorithm
+#' @param maxi maximum number of iterations in EM algorithm
+#' @param tau threshold to stop EM algorithm
+#'
#' @return the grid of regularization parameters
+#'
#' @export
-#-----------------------------------------------------------------------
-gridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, tau)
+computeGridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, tau)
{
n = nrow(X)
p = dim(phiInit)[1]
m = dim(phiInit)[2]
k = dim(phiInit)[3]
- #list_EMG = .Call("EMGLLF_core",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
list_EMG = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
grid = array(0, dim=c(p,m,k))
for (i in 1:p)
}
grid = unique(grid)
grid = grid[grid <=1]
-
- return(grid)
+ grid
}